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Text Analysis and Spatial Data for Economists

Description

Over the past years, the availability of new data through the digitalization of legal, political, journalistic corpora, as well as the release of fine-grained spatial data, has allowed researchers to answer new research questions and to explore novel causal inference techniques.

The aim of this course is to introduce students to the quantitative analysis of textual data and to the use of spatial datasets. We will cover both applications in the recent empirical research and the implementation of text and spatial data analysis techniques through hands-on experiences using the R statistical programming language (for text analysis) and QGIS (for spatial data).

The course will cover – among others – the following topics:

  • Web Scraping and automating collection of online data, including social media data
  • Quantitative text analysis
  • Spatial data analysis: spatial join, buffer, distances and elevation
  • Raster data

References
There is no textbook for this course. All slides and sample programs will be published on iCorsi.

 

Requested Material
Students should bring a laptop to all classes.

People

 

Parchet R.

Course director

Gessler T.

Teacher

Additional information

Semester
Spring
Academic year
2019-2020
ECTS
6
Language
English
Education
Master of Science in Economics, Core course, Minor in Data Science, 1st year